The Role of the Monthly ENSO in Forecasting the Daily Baltic Dry Index

Elie Bouri (), Rangan Gupta and Luca Rossini
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Elie Bouri: Lebanese American University, Lebanon

No 202229, Working Papers from University of Pretoria, Department of Economics

Abstract: Using Bayesian Reverse Unrestricted-Mixed Data Sampling (RU-MIDAS) models, we predict the daily Baltic Dry Index (BDI) based on the monthly information content of the El Nino Southern Oscillation (ENSO) from January, 1985 to February, 2022. The results show that the Oceanic Nino Index (ONI) capturing the ENSO produces statistically significant forecast gains in terms of both point and density forecasts for the BDI, relative to a constant-mean benchmark model, at both short and long forecast horizons (i.e., one to twenty one-day-ahead). Notably, these gains primarily emanate from the El Nino rather than La Nina phase of the ENSO.

Keywords: Baltic Dry Index (BDI); El Nino Southern Oscillation (ENSO); Reverse Unrestricted- Mixed Data Sampling (RU-MIDAS) Models; Forecasting (search for similar items in EconPapers)
JEL-codes: C22 C53 Q02 Q54 (search for similar items in EconPapers)
Pages: 11 pages
Date: 2022-06
New Economics Papers: this item is included in nep-dem and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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